{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\" style=\"color:green\">Matplotlib Tutorial: pyplot.plot format strings and other options</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=[1,2,3,4,5,6,7]\n",
    "y=[50,51,52,48,47,49,46]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c806e436d8>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V06fDz34WVmIPGhQ7mto57jiYOzck+kJX7ayhjF7E7GjgqtSsoTOB4YRxgxVA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\ngYhIkVMiEBEpckoEIiJFTolARKTI/T/nTyGDI5qkHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c806e43128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Day')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Weather')\n",
    "\n",
    "plt.plot(x,y,'b+--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c806da3898>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ggRP+1fdRNgK/zC4G7q6qf1/kQQz6Nul+kPg+4HBVvafveWaR5OlJzuqufx/wM8CX+51q\nOpt8CPrSSXJG9wN3uiWKi4Cle3dYVf0b8K9Jntfd9Apg6d488BhvZMHLLTDbh0RviyQfAl4GnJ3k\nAWClqt7X71RTuQC4HLi3W3sG+O2q+mSPM03rPOD67qf2TwA+UlVL/Xa/RpwLHNo4d2AXcENV3dzv\nSFN7OxsfQv8k4CvAL/U8z9SSPAV4JfArCz/WTn/boiRpMi65SFIjDLokNcKgS1IjDLokNcKgS1Ij\nDLokNcKgS1IjDLokNeL/APhe54r5A539AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c806d22438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same effect as 'b+' format string\n",
    "plt.plot(x,y,color='blue',marker='+',linestyle='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">markersize</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c8059f7160>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jK4C/med2LPQt1M033wJ8qKpu7TvPLHT/FD4AvLznKNO6GLiim3v+K+CSJH/Zb6TpVdXh\n7ucRYD9wUb+JpvIg8OAJ/+r7KOsFv8heAdxTVf8+z41Y6Fuk+yDx/cDBqnpv33k2I8nTkzy1e/5D\nwM8DX+031XQ2uAn6wklyRveBO90UxWXAwp0dVlX/Bvxrkud0qy4FFu7kgcd4A3OeboHN3SR6SyT5\nMPAS4OwkDwJLVfX+flNN5WLgKuDL3dwzwO9U1Sd7zDSt84Cbuk/tnwB8pKoW+nS/RpwL7F8/dmAH\ncHNV3d5vpKm9nfWb0D8JeAB4U895ppbkKcDLgF+b+7a2+2mLkqTxOOUiSY2w0CWpERa6JDXCQpek\nRljoktQIC12SGmGhS1IjLHRJasT/AVtb4BymT5v5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c8059695c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,color='blue',marker='+',linestyle='',markersize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">alpha property to control transparency of line chart</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c805b212b0>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PzB2ZuSEzNwE3Aw9m5i3APwJX1zd7EzDasiklqQ11d3azdfNWdl63k22XbGPyhUnGj4y3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\nVIj/A8m1OpFNNEQQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c805ad5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'g<',alpha=0.5) # alpha can be specified on a scale 0 to 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
